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Fix some lint errors in image_ops_test.py and word2vec_basic.py

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Change: 110727357
This commit is contained in:
Vijay Vasudevan 2015-12-22 15:06:34 -08:00
parent fc4063af80
commit 10f2363724
17 changed files with 123 additions and 145 deletions
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37
README.md
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@ -16,11 +16,8 @@ organization for the purposes of conducting machine learning and deep neural
networks research. The system is general enough to be applicable in a wide
variety of other domains, as well.
**Note: Currently we do not accept pull requests on github -- see
[CONTRIBUTING.md](CONTRIBUTING.md) for information on how to contribute code
changes to TensorFlow through
[tensorflow.googlesource.com](https://tensorflow.googlesource.com/tensorflow)**
**If you'd like to contribute to tensorflow, be sure to review the [contribution
guidelines](CONTRIBUTING.md).**
**We use [github issues](https://github.com/tensorflow/tensorflow/issues) for
tracking requests and bugs, but please see
@ -29,35 +26,7 @@ and discussion.**
# Download and Setup
To install the CPU version of TensorFlow using a binary package, see the
instructions below. For more detailed installation instructions, including
installing from source, GPU-enabled support, etc., see
[here](tensorflow/g3doc/get_started/os_setup.md).
## Binary Installation
The TensorFlow Python API supports Python 2.7 and Python 3.3+.
The simplest way to install TensorFlow is using
[pip](https://pypi.python.org/pypi/pip) for both Linux and Mac.
For the GPU-enabled version, or if you encounter installation errors, or for
more detailed installation instructions, see
[here](tensorflow/g3doc/get_started/os_setup.md#detailed_install).
### Ubuntu/Linux 64-bit
```bash
# For CPU-only version
$ pip install https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.5.0-cp27-none-linux_x86_64.whl
```
### Mac OS X
```bash
# Only CPU-version is available at the moment.
$ pip install https://storage.googleapis.com/tensorflow/mac/tensorflow-0.5.0-py2-none-any.whl
```
See [install instructions](tensorflow/g3doc/get_started/os_setup.md).
### Try your first TensorFlow program

1
tensorflow/core/BUILD
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@ -349,6 +349,7 @@ tf_gpu_kernel_library(
visibility = ["//visibility:public"],
deps = [
":cuda",
":framework",
"//third_party/eigen3",
],
)

7
tensorflow/core/kernels/resize_area_op.cc
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@ -144,11 +144,8 @@ class ResizeAreaOp : public OpKernel {
.HostMemory("size"), \
ResizeAreaOp<CPUDevice, T>);
REGISTER_KERNEL(uint8);
REGISTER_KERNEL(int8);
REGISTER_KERNEL(int32);
REGISTER_KERNEL(float);
REGISTER_KERNEL(double);
TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNEL);
#undef REGISTER_KERNEL
} // namespace tensorflow

7
tensorflow/core/kernels/resize_bicubic_op.cc
View File

@ -131,11 +131,8 @@ class ResizeBicubicOp : public OpKernel {
.HostMemory("size"), \
ResizeBicubicOp<CPUDevice, T>);
REGISTER_KERNEL(uint8);
REGISTER_KERNEL(int8);
REGISTER_KERNEL(int32);
REGISTER_KERNEL(float);
REGISTER_KERNEL(double);
TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNEL);
#undef REGISTER_KERNEL
} // namespace tensorflow

7
tensorflow/core/kernels/resize_bilinear_op.cc
View File

@ -215,11 +215,8 @@ class ResizeBilinearOpGrad : public OpKernel {
.HostMemory("size"), \
ResizeBilinearOp<CPUDevice, T>);
REGISTER_KERNEL(uint8);
REGISTER_KERNEL(int8);
REGISTER_KERNEL(int32);
REGISTER_KERNEL(float);
REGISTER_KERNEL(double);
TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNEL);
#undef REGISTER_KERNEL
REGISTER_KERNEL_BUILDER(Name("ResizeBilinearGrad")

7
tensorflow/core/kernels/resize_nearest_neighbor_op.cc
View File

@ -178,11 +178,8 @@ class ResizeNearestNeighborOpGrad : public OpKernel {
.HostMemory("size"), \
ResizeNearestNeighborOpGrad<CPUDevice, T>);
REGISTER_KERNEL(uint8);
REGISTER_KERNEL(int8);
REGISTER_KERNEL(int32);
REGISTER_KERNEL(float);
REGISTER_KERNEL(double);
TF_CALL_REAL_NUMBER_TYPES(REGISTER_KERNEL);
#undef REGISTER_KERNEL
} // namespace tensorflow

8
tensorflow/core/ops/image_ops.cc
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@ -22,7 +22,7 @@ REGISTER_OP("ResizeArea")
.Input("images: T")
.Input("size: int32")
.Output("resized_images: float")
.Attr("T: {uint8, int8, int32, float, double}")
.Attr("T: {uint8, int8, int16, int32, int64, float, double}")
.Doc(R"doc(
Resize `images` to `size` using area interpolation.
@ -40,7 +40,7 @@ REGISTER_OP("ResizeBicubic")
.Input("images: T")
.Input("size: int32")
.Output("resized_images: float")
.Attr("T: {uint8, int8, int32, float, double}")
.Attr("T: {uint8, int8, int16, int32, int64, float, double}")
.Doc(R"doc(
Resize `images` to `size` using bicubic interpolation.
@ -58,7 +58,7 @@ REGISTER_OP("ResizeBilinear")
.Input("images: T")
.Input("size: int32")
.Output("resized_images: float")
.Attr("T: {uint8, int8, int32, float, double}")
.Attr("T: {uint8, int8, int16, int32, int64, float, double}")
.Doc(R"doc(
Resize `images` to `size` using bilinear interpolation.
@ -93,7 +93,7 @@ REGISTER_OP("ResizeNearestNeighbor")
.Input("images: T")
.Input("size: int32")
.Output("resized_images: T")
.Attr("T: {uint8, int8, int32, float, double}")
.Attr("T: {uint8, int8, int16, int32, int64, float, double}")
.Doc(R"doc(
Resize `images` to `size` using nearest neighbor interpolation.

8
tensorflow/core/ops/ops.pbtxt
View File

@ -5949,7 +5949,9 @@ op {
list {
type: DT_UINT8
type: DT_INT8
type: DT_INT16
type: DT_INT32
type: DT_INT64
type: DT_FLOAT
type: DT_DOUBLE
}
@ -5982,7 +5984,9 @@ op {
list {
type: DT_UINT8
type: DT_INT8
type: DT_INT16
type: DT_INT32
type: DT_INT64
type: DT_FLOAT
type: DT_DOUBLE
}
@ -6015,7 +6019,9 @@ op {
list {
type: DT_UINT8
type: DT_INT8
type: DT_INT16
type: DT_INT32
type: DT_INT64
type: DT_FLOAT
type: DT_DOUBLE
}
@ -6077,7 +6083,9 @@ op {
list {
type: DT_UINT8
type: DT_INT8
type: DT_INT16
type: DT_INT32
type: DT_INT64
type: DT_FLOAT
type: DT_DOUBLE
}

20
tensorflow/examples/tutorials/word2vec/word2vec_basic.py
View File

@ -141,16 +141,18 @@ with graph.as_default():
train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
valid_dataset = tf.constant(valid_examples, dtype=tf.int32)
# Construct the variables.
embeddings = tf.Variable(
tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
nce_weights = tf.Variable(
tf.truncated_normal([vocabulary_size, embedding_size],
stddev=1.0 / math.sqrt(embedding_size)))
nce_biases = tf.Variable(tf.zeros([vocabulary_size]))
# Ops and variables pinned to the CPU because of missing GPU implementation
with tf.device('/cpu:0'):
# Look up embeddings for inputs.
embeddings = tf.Variable(
tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
embed = tf.nn.embedding_lookup(embeddings, train_inputs)
# Look up embeddings for inputs.
embed = tf.nn.embedding_lookup(embeddings, train_inputs)
# Construct the variables for the NCE loss
nce_weights = tf.Variable(
tf.truncated_normal([vocabulary_size, embedding_size],
stddev=1.0 / math.sqrt(embedding_size)))
nce_biases = tf.Variable(tf.zeros([vocabulary_size]))
# Compute the average NCE loss for the batch.
# tf.nce_loss automatically draws a new sample of the negative labels each

2
tensorflow/g3doc/api_docs/python/framework.md
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@ -1408,7 +1408,7 @@ and Python scalars. For example:
```python
import numpy as np
array = np.random.rand((32, 100, 100))
array = np.random.rand(32, 100, 100)
def my_func(arg):
arg = tf.convert_to_tensor(arg, dtype=tf.float32)

10
tensorflow/g3doc/resources/dims_types.md
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@ -54,14 +54,14 @@ Data type | Python type | Description
--- | --- | ---
`DT_FLOAT` | `tf.float32` | 32 bits floating point.
`DT_DOUBLE` | `tf.float64` | 64 bits floating point.
`DT_INT64` | `tf.int64` | 64 bits signed integer.
`DT_INT32` | `tf.int32` | 32 bits signed integer.
`DT_INT16` | `tf.int16` | 16 bits signed integer.
`DT_INT8` | `tf.int8` | 8 bits signed integer.
`DT_INT16` | `tf.int16` | 16 bits signed integer.
`DT_INT32` | `tf.int32` | 32 bits signed integer.
`DT_INT64` | `tf.int64` | 64 bits signed integer.
`DT_UINT8` | `tf.uint8` | 8 bits unsigned integer.
`DT_STRING` | `tf.string` | Variable length byte arrays. Each element of a Tensor is a byte array.
`DT_BOOL` | `tf.bool` | Boolean.
`DT_COMPLEX64` | `tf.complex64` | Complex number made of two 32 bits floating points: real and imaginary parts.
`DT_QINT32` | `tf.qint32` | 32 bits signed integer used in quantized Ops.
`DT_QINT8` | `tf.qint8` | 8 bits signed integer used in quantized Ops.
`DT_QUINT8` | `tf.quint8` | 8 bits unsigned integer used in quantized Ops.
`DT_QINT32` | `tf.qint32` | 32 bits signed integer used in quantized Ops.
`DT_QUINT8` | `tf.quint8` | 8 bits unsigned integer used in quantized Ops.

2
tensorflow/g3doc/tutorials/deep_cnn/index.md
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@ -126,7 +126,7 @@ artificially increase the data set size:
* [Randomly flip](../../api_docs/python/image.md#random_flip_left_right) the image from left to right.
* Randomly distort the [image brightness](../../api_docs/python/image.md#random_brightness).
* Randomly distort the [image contrast](../../api_docs/python/image.md#tf_image_random_contrast).
* Randomly distort the [image contrast](../../api_docs/python/image.md#random_contrast).
Please see the [Images](../../api_docs/python/image.md) page for the list of
available distortions. We also attach an

2
tensorflow/python/framework/ops.py
View File

@ -486,7 +486,7 @@ def convert_to_tensor(value, dtype=None, name=None, as_ref=False):
```python
import numpy as np
array = np.random.rand((32, 100, 100))
array = np.random.rand(32, 100, 100)
def my_func(arg):
arg = tf.convert_to_tensor(arg, dtype=tf.float32)

139
tensorflow/python/ops/image_ops_test.py
View File

@ -564,49 +564,56 @@ class ResizeImagesTest(test_util.TensorFlowTestCase):
image_ops.ResizeMethod.BICUBIC,
image_ops.ResizeMethod.AREA]
TYPES = [np.uint8, np.int8, np.int16, np.int32, np.int64,
np.float, np.double]
def testNoOp(self):
img_shape = [1, 6, 4, 1]
single_shape = [6, 4, 1]
data = [128, 128, 64, 64,
128, 128, 64, 64,
64, 64, 128, 128,
64, 64, 128, 128,
# This test is also conducted with int8, so 127 is the maximum
# value that can be used.
data = [127, 127, 64, 64,
127, 127, 64, 64,
64, 64, 127, 127,
64, 64, 127, 127,
50, 50, 100, 100,
50, 50, 100, 100]
img_np = np.array(data, dtype=np.uint8).reshape(img_shape)
target_height = 6
target_width = 4
for opt in self.OPTIONS:
with self.test_session() as sess:
image = constant_op.constant(img_np, shape=img_shape)
y = image_ops.resize_images(image, target_height, target_width, opt)
yshape = array_ops.shape(y)
resized, newshape = sess.run([y, yshape])
self.assertAllEqual(img_shape, newshape)
self.assertAllClose(resized, img_np, atol=1e-5)
for nptype in self.TYPES:
img_np = np.array(data, dtype=nptype).reshape(img_shape)
# Resizing with a single image must leave the shape unchanged also.
with self.test_session():
img_single = img_np.reshape(single_shape)
image = constant_op.constant(img_single, shape=single_shape)
y = image_ops.resize_images(image, target_height, target_width,
self.OPTIONS[0])
yshape = array_ops.shape(y)
newshape = yshape.eval()
self.assertAllEqual(single_shape, newshape)
for opt in self.OPTIONS:
with self.test_session() as sess:
image = constant_op.constant(img_np, shape=img_shape)
y = image_ops.resize_images(image, target_height, target_width, opt)
yshape = array_ops.shape(y)
resized, newshape = sess.run([y, yshape])
self.assertAllEqual(img_shape, newshape)
self.assertAllClose(resized, img_np, atol=1e-5)
# Resizing with a single image must leave the shape unchanged also.
with self.test_session():
img_single = img_np.reshape(single_shape)
image = constant_op.constant(img_single, shape=single_shape)
y = image_ops.resize_images(image, target_height, target_width,
self.OPTIONS[0])
yshape = array_ops.shape(y)
newshape = yshape.eval()
self.assertAllEqual(single_shape, newshape)
def testResizeDown(self):
data = [128, 128, 64, 64,
128, 128, 64, 64,
64, 64, 128, 128,
64, 64, 128, 128,
# This test is also conducted with int8, so 127 is the maximum
# value that can be used.
data = [127, 127, 64, 64,
127, 127, 64, 64,
64, 64, 127, 127,
64, 64, 127, 127,
50, 50, 100, 100,
50, 50, 100, 100]
expected_data = [128, 64,
64, 128,
expected_data = [127, 64,
64, 127,
50, 100]
target_height = 3
target_width = 2
@ -617,59 +624,61 @@ class ResizeImagesTest(test_util.TensorFlowTestCase):
[target_height, target_width, 1]]
for target_shape, img_shape in zip(target_shapes, img_shapes):
img_np = np.array(data, dtype=np.uint8).reshape(img_shape)
for opt in self.OPTIONS:
with self.test_session():
image = constant_op.constant(img_np, shape=img_shape)
y = image_ops.resize_images(image, target_height, target_width, opt)
expected = np.array(expected_data).reshape(target_shape)
resized = y.eval()
self.assertAllClose(resized, expected, atol=1e-5)
for nptype in self.TYPES:
img_np = np.array(data, dtype=nptype).reshape(img_shape)
for opt in self.OPTIONS:
with self.test_session():
image = constant_op.constant(img_np, shape=img_shape)
y = image_ops.resize_images(image, target_height, target_width, opt)
expected = np.array(expected_data).reshape(target_shape)
resized = y.eval()
self.assertAllClose(resized, expected, atol=1e-5)
def testResizeUp(self):
img_shape = [1, 3, 2, 1]
data = [128, 64,
64, 128,
data = [64, 32,
32, 64,
50, 100]
img_np = np.array(data, dtype=np.uint8).reshape(img_shape)
target_height = 6
target_width = 4
expected_data = {}
expected_data[image_ops.ResizeMethod.BILINEAR] = [
128.0, 96.0, 64.0, 64.0,
96.0, 96.0, 96.0, 96.0,
64.0, 96.0, 128.0, 128.0,
57.0, 85.5, 114.0, 114.0,
64.0, 48.0, 32.0, 32.0,
48.0, 48.0, 48.0, 48.0,
32.0, 48.0, 64.0, 64.0,
41.0, 61.5, 82.0, 82.0,
50.0, 75.0, 100.0, 100.0,
50.0, 75.0, 100.0, 100.0]
expected_data[image_ops.ResizeMethod.NEAREST_NEIGHBOR] = [
128.0, 128.0, 64.0, 64.0,
128.0, 128.0, 64.0, 64.0,
64.0, 64.0, 128.0, 128.0,
64.0, 64.0, 128.0, 128.0,
64.0, 64.0, 32.0, 32.0,
64.0, 64.0, 32.0, 32.0,
32.0, 32.0, 64.0, 64.0,
32.0, 32.0, 64.0, 64.0,
50.0, 50.0, 100.0, 100.0,
50.0, 50.0, 100.0, 100.0]
expected_data[image_ops.ResizeMethod.AREA] = [
128.0, 128.0, 64.0, 64.0,
128.0, 128.0, 64.0, 64.0,
64.0, 64.0, 128.0, 128.0,
64.0, 64.0, 128.0, 128.0,
64.0, 64.0, 32.0, 32.0,
64.0, 64.0, 32.0, 32.0,
32.0, 32.0, 64.0, 64.0,
32.0, 32.0, 64.0, 64.0,
50.0, 50.0, 100.0, 100.0,
50.0, 50.0, 100.0, 100.0]
for opt in [
image_ops.ResizeMethod.BILINEAR,
image_ops.ResizeMethod.NEAREST_NEIGHBOR,
image_ops.ResizeMethod.AREA]:
with self.test_session():
image = constant_op.constant(img_np, shape=img_shape)
y = image_ops.resize_images(image, target_height, target_width, opt)
resized = y.eval()
expected = np.array(expected_data[opt]).reshape(
[1, target_height, target_width, 1])
self.assertAllClose(resized, expected, atol=1e-05)
for nptype in self.TYPES:
for opt in [
image_ops.ResizeMethod.BILINEAR,
image_ops.ResizeMethod.NEAREST_NEIGHBOR,
image_ops.ResizeMethod.AREA]:
with self.test_session():
img_np = np.array(data, dtype=nptype).reshape(img_shape)
image = constant_op.constant(img_np, shape=img_shape)
y = image_ops.resize_images(image, target_height, target_width, opt)
resized = y.eval()
expected = np.array(expected_data[opt]).reshape(
[1, target_height, target_width, 1])
self.assertAllClose(resized, expected, atol=1e-05)
def testResizeUpBicubic(self):
img_shape = [1, 6, 6, 1]

2
tensorflow/python/ops/template.py
View File

@ -187,7 +187,7 @@ class Template(object):
"meant tf.get_variable: %s",
variables[vars_at_start:])
return result
except Exception, exc:
except Exception as exc:
# Reraise the exception, but append the original definition to the
# trace.
args = exc.args

8
tensorflow/python/training/coordinator.py
View File

@ -70,7 +70,7 @@ class Coordinator(object):
try:
while not coord.should_stop():
...do some work...
except Exception, e:
except Exception as e:
coord.request_stop(e)
```
@ -85,7 +85,7 @@ class Coordinator(object):
...start thread N...(coord, ...)
# Wait for all the threads to terminate.
coord.join(threads)
except Exception, e:
except Exception as e:
...exception that was passed to coord.request_stop()
```
@ -188,7 +188,7 @@ class Coordinator(object):
```python
try:
...body...
exception Exception, ex:
exception Exception as ex:
coord.request_stop(ex)
```
@ -198,7 +198,7 @@ class Coordinator(object):
# pylint: disable=broad-except
try:
yield
except Exception, ex:
except Exception as ex:
self.request_stop(ex)
# pylint: enable=broad-except

1
tensorflow/stream_executor/BUILD
View File

@ -19,6 +19,7 @@ cc_library(
),
hdrs = glob([
"*.h",
"cuda/*.h",
"lib/*.h",
"platform/**/*.h",
]),